1. Field
This invention relates generally to content distribution via distributed computing applications and more particularly to content distribution to geolocations at which events are inferred to be occurring based on data reported from mobile computing devices at the events.
2. Description of the Related Art
Content targeting according to geolocation is used for a variety of purposes. For instance, users may wish to view information on mobile devices, like cell phones, selected according to the user's current position, like reviews of nearby restaurants, offers (e.g., coupons or deals) for nearby merchants, and news stories pertaining to their geographic area. In many cases, content is selected based on the user's location (e.g., geotargeted) according to attributes of the user's geographic location (i.e., aspects of the location other than the geographic coordinates), like whether the user is in a particular type of store, or likely engaging in a particular activity known to occur at the geolocation.
Many systems for targeting content according to attributes of geolocations require that geographic areas be labeled in advance. For instance, business listings and geographic information systems often include extensive repositories of polygons defining the boundaries of various geographic areas (like stores, parks, bars, and restaurants) and attributes of those areas (like amenities and activities associated with the location). In many cases, extensive investments are made to construct these data collections by researching and tracking what sorts of activities and things are at the locations. Such systems, however, do not work well for transient attributes of geolocations, like events where crowds form. In many cases, the event is over before the database can be updated.
Aggravating these issues, in many cases, wireless media near events is crowded and unreliable, and users often place extra strain on their battery when attending events, e.g., by repeatedly seeking access to the wireless media and to coordinate among friends. Thus, detecting events where people form crowds and geotargeting content into the geographic area quickly enough that the crowd has not dispersed, in a battery sensitive fashion, without reliable wireless communication is particularly difficult.
The problem is made all the more challenging by the risk of false positives. In many cases, it is desirable to target content to events based on those events signaling something unique and special about the attendees' experience, like a concert or sporting event, rather than a crowded morning commute. Yet, people form crowds in the course of going about their daily routine, and those crowds do not correspond to these events (e.g., transient special events) warranting content targeting. In both cases, i.e., routinely formed crowds and special events, the signals received by, and data emitted by, wireless mobile devices are similar and computationally challenging to distinguish.